事件驱动的带执行器无线传感器网络分簇算法研究

带有执行器的无线传感器网络是指在传统无线传感器网络中加入执行节点,形成传感器节点、执行节点和基站共同构成的三层监控网络。根据执行器在能量、计算能力和感知能力方面的优势,提出建立应用于事件调度的双环分簇算法。算法将执行器连接成双环结构,提升网络在线扩展能力的同时,也为无线传感器网络满足事件驱动构建基础。仿真实验证明,此算法能够有效降低网络能耗,随着节点数目的增加和监控领域的扩大,表现更加凸出。     

基于空间相关性的事件驱动无线传感器网络分簇算法

分簇算法是传感器网络中减少能量消耗的一种关键技术,它能够增强网络的扩展性和延长网络的生存时间。针对传感器节点数据的空间相关性,该文提出了一种新的基于空间相关性的事件驱动传感器网络分簇算法。算法根据用户要求的误差门限及结合节点数据的空间相关性马尔可夫模型,将事件感知区域划分成虚拟极坐标等价层。每个等价层选取层内当前剩余能量最大的节点作为簇头,网络通过移动代理收集簇头感知信息,该方法减少了传输数据量,有效节省了网络能量。     

基于空间相关性的事件驱动无线传感器网络分簇算法

分簇算法是传感器网络中减少能量消耗的一种关键技术,它能够增强网络的扩展性和延长网络的生存时间。针对传感器节点数据的空间相关性,该文提出了一种新的基于空间相关性的事件驱动传感器网络分簇算法。算法根据用户要求的误差门限及结合节点数据的空间相关性马尔可夫模型,将事件感知区域划分成虚拟极坐标等价层。每个等价层选取层内当前剩余能量最大的节点作为簇头,网络通过移动代理收集簇头感知信息,该方法减少了传输数据量,有效节省了网络能量。     

基于动态簇半径的非均匀分簇算法

在无线传感器网络分簇路由算法中,针对节点能耗不均衡所引发的"热区"问题,提出了基于动态簇半径的非均匀分簇算法(UCDCR)。该算法在簇组建阶段,对网络进行区域划分,不同区域的候选簇首通过簇竞争半径来构建大小不同的簇,使簇首随网络的运行动态的改变簇竞争半径,为数据转发预留更多能量。仿真结果表明:与EEUC算法和CUCRA算法相比,UCDCR算法更加有效地均衡了节点能耗,延长了网络生命的周期。     

新的无线传感器网络分簇算法

针对无线传感器网络节点能量受限的特点,提出了一种响应式分布分簇算法(RDCA,responsive distributedclustering algorithm).该算法不需预先得知节点自身及其他节点的位置信息,而仅根据局部拓扑信息快速进行分布式的簇头选举,并根据代价函数进行簇的划分,适用于周期性获取信息的无线传感器网络.分析与仿真表明,该算法具有良好的负载平衡性能和较小的协议开销,与LEACH协议相比,能够减少能量消耗,网络生存期大约延长了40%.     

基于权值的无线传感器网络分簇算法

综述了无线传感器网络路由算法的主要成果,但重点分析更具有能量有效性的分簇路由算法,对各种算法的主要思想进行了性能评价,提出了一种新的算法.     

面向目标跟踪的无线传感器网络动态分簇

在考虑节点剩余能量和节点探测信号强度的基础上,提出了一种面向目标跟踪的无线传感器网络动态分簇方法。由于在簇首选择时考虑了节点的剩余能量,因此可以有效地避免簇首节点能量不足导致的跟踪失败。方法既能保证各个节点均衡地担任簇首节点,又能避免簇首切换过于频繁,因此可以延长网络的使用寿命。计算机仿真结果表明,在满足簇首节点能量需求和簇内节点大部分能观测到目标的情况下,应该取尽量小的能量阈值和尽量大的距离阈值。     

无线传感器网络中基于自适应定时器策略的分簇算法

提出了一种适用于无线传感器网络的基于自适应定时器策略的分簇算法,该算法通过自适应调整节点的等待时间来保证较多电池能量的节点有更大的机会成为簇首.理论分析表明该算法不仅实现了系统的动态负载均衡,并且能够通过参数的设置确保簇首的均匀分布.仿真结果显示该算法有效地延长了系统生命,提高了网络的能耗效率.     

基于扇形分簇的无线传感器网络路由算法

无线传感网络中低功耗自适应聚类分簇(LEACH)路由算法等概率选取簇首节点,容易导致整个网络节点能量损耗出现极端化,减少网络生存时间。为此,提出一种针对簇首节点选取和分簇的改进LEACH算法。该算法把整个网络区域分为四个扇形区域,在每个区域内独立进行分簇路由;然后基站根据节点剩余能量和与基站的距离进行簇首节点选择,节点根据簇首节点和基站接收信号强度选择路由方式,以均衡网络能量消耗。仿真结果表明,改进LEACH算法的网络寿命是原有LEACH算法的150%,数据吞吐量提升了3倍。     

无线传感器网络分簇算法分析与性能比较

文中在介绍无线传感器网络路由协议的基础上,重点分析了几种有代表性的分簇路由协议算法。然后对各种分簇算法从10个评价参数上进行了一个综合对比,总结了无线传感器网络现有分簇路由协议的优点和存在的问题。最后从网络安全性和协议的实用性等方面,并对无线传感器网络分簇路由协议算法进行了展望。     

Dual head static clustering algorithm for wireless sensor networks

Clustering of nodes is often used in wireless sensor networks to achieve data aggregation and reduce the number of nodes transmitting the data to the sink. This paper proposes a novel dual head static clustering algorithm (DHSCA) to equalise energy consumption by the sensor nodes and increase the wireless sensor network lifetime. Nodes are divided into static clusters based on their location to avoid the overhead of cluster re-formation in dynamic clustering. Two nodes in each cluster, selected on the basis of the their residual energy and their distance from the sink and other nodes in the cluster, are designated as cluster heads, one for data aggregation and the other for data transmission. This reduces energy consumption during intra-cluster and inter-cluster communication. A multi-hop technique avoiding the hot-spot problem is used to transmit the data to the sink. Experiments to observe the energy consumption patterns of the nodes and the fraction of packets successfully delivered using the DHSCA suggest improvements in energy consumption equalisation, which, in turn, enhances the lifetime of the network. The algorithm is shown to outperform all the other static clustering algorithms, while being comparable with the performance of the best dynamic algorithm.     

An energy efficient clustering routing algorithm for wireless sensor networks

~~An energy efficient clustering routing algorithm for wireless sensor networks1. Mainwaring A, Polastre J, Szewczyk R, et al. Wireless sensor networks for habitat monitoring. Proceedings of the ACM International Workshop on Wireless Sensor Networks and A…     

WSN中能量有效分簇多跳路由算法

针对现有无线传感器网络(WSN)分簇路由算法存在的能耗不均衡问题,提出一种能量有效分簇多跳路由算法,该算法包括两个方面:一是选举簇首时引入簇内平均剩余能量因子,根据上一轮结束后簇内各节点剩余能量和簇内节点的平均剩余能量的比值更新簇首在所有节点中所占的百分比;二是要求簇首根据MTE多跳路由协议与基站通信,从而均衡WSN整...     

Energy-efficiency clustering protocol in wireless sensor networks

The conventional clustering method has the unique potential to be the framework for power-conserving ad hoc networks. In this environment, studies on energy-efficient strategies such as sleeping mode and redirection have been reported, and recently some have even been adopted by some standards like Bluetooth and IEEE 802.11. However, consider wireless sensor networks. The devices employed are power-limited in nature, introducing the conventional clustering approach to the sensor networks provides a unique challenge due to the fact that cluster-heads, which are communication centers by default, tend to be heavily utilized and thus drained of their battery power rapidly. In this paper, we introduce a re-clustering strategy and a redirection scheme for cluster-based wireless sensor networks in order to address the power-conserving issues in such networks, while maintaining the merits of a clustering approach. Based on a practical energy model, simulation results show that the improved clustering method can obtain a longer lifetime when compared with the conventional clustering method.     

A novel approach based on bio-inspired efficient clustering algorithm for large-scale heterogeneous wireless sensor networks

In large-scale heterogeneous wireless sensor networks (WSNs), clustering is particularly significant for lowering sensor nodes (SNs) energy consumption and creating algorithm more energy efficient. The selection of cluster heads (CHs) is a crucial task in the clustering method. In this paper, optimised K-means clustering algorithm and optimised K-means based modified intelligent CH selection based on BFOA for large-scale network (lar-OK-MICHB) is hybridised for CH selection process. Here, we utilised the extended capabilities of OK-MICHB algorithm for large-scale network. Furthermore, in many applications where energy is a primary constraint, such as military surveillance and natural disaster prediction, the stability region is also a significant factor, with a longer network lifespan being a primary requirement. In the proposed approach, only the CH selection is made after every round in place of cluster and CH change as done in conventional hierarchical algorithm. The simulation results reveal that, while keeping the distributive structure of WSNs, suggested lar-OKMIDEEC can locate real greater leftover energy nodes for selection of CH without utilising randomise or estimated procedures. Furthermore, as compared with the multi-level MIDEEC protocol, this offers a larger stability region with 68.96% increment, more consistent selection of CH in every round, and greater packets (i.e., in numbers) received at the base station (BS) with a longer network lifetime with 327% increment.     

Node correlation clustering algorithm for wireless multimedia sensor networks based on overlapped FoVs

In this paper, a clustering algorithm is proposed based on the high correlation among the overlapped field of views for the wireless multimedia sensor networks. Firstly, by calculating the area of the overlapped field of views （FoVs） based on the gird method, node correlations have been obtained. Then, the algorithm utilizes the node correlations to partition the network region in which there are high correlation multimedia sensor nodes. Meanwhile, in order to minimize the energy consumption for transmitting images, the strategy of the cluster heads election is proposed based on the cost estimation, which consists of signal strength and residual energy as well as the node correlation. Simulation results show that the proposed algorithm can balance the energy consumption and extend the network lifetime effectively.     

Pareto-optimal clustering scheme using data aggregation for wireless sensor networks

The presence of cluster heads (CHs) in a clustered wireless sensor network (WSN) leads to improved data aggregation and enhanced network lifetime. Thus, the selection of appropriate CHs in WSNs is a challenging task, which needs to be addressed. A multicriterion decision-making approach for the selection of CHs is presented using Pareto-optimal theory and technique for order preference by similarity to ideal solution (TOPSIS) methods. CHs are selected using three criteria including energy, cluster density and distance from the sink. The overall network lifetime in this method with 50% data aggregation after simulations is 81% higher than that of distributed hierarchical agglomerative clustering in similar environment and with same set of parameters. Optimum number of clusters is estimated using TOPSIS technique and found to be 9–11 for effective energy usage in WSNs.     

采用自适应退避机制的无线传感网分簇算法

传统的LEACH算法因为簇首选举的随机性而导致节点能量分布不均,从而影响网络寿命和系统吞吐量,在此基础之上提出了一种基于自适应退避机制的分簇算法,通过设定一个能量门限来衡量簇首的健康度,并采用退避机制来使那些趋于衰亡的簇首能找到合适的继任者来担当簇首,保证数据传输的可靠性.仿真实验显示,该算法能够较LEACH获得更长的网络寿命,并提高了网络的吞吐量.     

基于动态分区的无线传感器网络非均匀成簇路由协议简

针对节点负载不均而形成的“热区”问题,提出了一种基于动态分区负载均衡的分布式成簇路由协议(UCDP)。其核心思想是：将网络合理化地动态分区,使距离基站较近的区面积较小,从而减少需要承担转发任务节点的区内通信开销,节省更多的能量供数据转发使用;综合考虑距离因子和剩余能量因子进行区内非均匀成簇;有机结合簇内单跳和区间转发,区头与簇头共同协作进行路由传输。实验表明,协议具有较好的稳定性,显著延长了网络的生存周期。     

Distributed fuzzy approach to unequal clustering and routing algorithm for wireless sensor networks

Due to inherent issue of energy limitation in sensor nodes, the energy conservation is the primary concern for large‐scale wireless sensor networks. Cluster‐based routing has been found to be an effective mechanism to reduce the energy consumption of sensor nodes. In clustered wireless sensor networks, the network is divided into a set of clusters; each cluster has a coordinator, called cluster head (CH). Each node of a cluster transmits its collected information to its CH that in turn aggregates the received information and sends it to the base station directly or via other CHs. In multihop communication, the CHs closer to the base station are burdened with high relay load; as a result, their energy depletes much faster as compared with other CHs. This problem is termed as the hot spot problem. In this paper, a distributed fuzzy logic‐based unequal clustering approach and routing algorithm (DFCR) is proposed to solve this problem. Based on the cluster design, a multihop routing algorithm is also proposed, which is both energy efficient and energy balancing. The simulation results reinforce the efficiency of the proposed DFCR algorithm over the state‐of‐the‐art algorithms, ie, energy‐aware fuzzy approach to unequal clustering, energy‐aware distributed clustering, and energy‐aware routing algorithm, in terms of different performance parameters like energy efficiency and network lifetime.